通过无监督聚类分析威尔士智力障碍者长期病共现轨迹，揭示不同人群的疾病模式。
Temporal Patterns of Multiple Long-Term Conditions in Individuals with Intellectual Disability Living in Wales: An Unsupervised Clustering Approach to Disease Trajectories
- 基于电子病历用谱聚类识别疾病轨迹共现模式
- 男性45岁以下以神经类病为主（32.4%），45岁以上以循环系统病为主（51.8%）
- 女性45岁以上分两类：循环或消化/骨骼系统病，适用于精准医疗研究
识别智力障碍（ID）个体中多种长期疾病（MLTC）的共现模式对有效医疗管理至关重要。这些个体常出现更早发病和更高患病率，但具体共现模式尚未明确。本研究利用威尔士13069名ID患者（2000–2021年）的电子健康记录（EHRs），采用无监督方法分析疾病轨迹共现模式。评估了疾病关联性与时间方向性后，使用谱聚类进行分组。研究对象中男性占52.3%，女性占47.7%，平均每人有4.5种疾病。45岁以下男性形成单一集群，以神经类疾病（32.4%）为主；45岁以上男性分为三类，最大一类以循环系统疾病（51.8%）为主。45岁以下女性为一类，消化系统疾病（24.6%）最常见；45岁及以上女性分为两类：一类以循环系统疾病（34.1%）为主，另一类以消化系统（25.9%）和骨骼肌肉系统（21.9%）疾病为主。精神疾病、癫痫和胃食管反流在各组中均较常见。这些集群为理解ID人群疾病进展提供了依据，有助于制定针对性干预与个性化医疗策略。
原文摘要 · Abstract (English)
Identifying and understanding the co-occurrence of multiple long-term conditions (MLTC) in individuals with intellectual disabilities (ID) is vital for effective healthcare management. These individuals often face earlier onset and higher prevalence of MLTCs, yet specific co-occurrence patterns remain unexplored. This study applies an unsupervised approach to characterise MLTC clusters based on shared disease trajectories using electronic health records (EHRs) from 13069 individuals with ID in Wales (2000-2021). Disease associations and temporal directionality were assessed, followed by spectral clustering to group shared trajectories. The population consisted of 52.3% males and 47.7% females, with an average of 4.5 conditions per patient. Males under 45 formed a single cluster dominated by neurological conditions (32.4%), while males above 45 had three clusters, the largest characterised circulatory (51.8%). Females under 45 formed one cluster with digestive conditions (24.6%) as most prevalent, while those aged 45 and older showed two clusters: one dominated by circulatory (34.1%), and the other by digestive (25.9%) and musculoskeletal (21.9%) system conditions. Mental illness, epilepsy, and reflux were common across groups. These clusters offer insights into disease progression in individuals with ID, informing targeted interventions and personalised healthcare strategies.
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